Weight bank addition photonic accelerator for artificial intelligence

Wenwen Zhang; Hao Zhang; Jing Wang

doi:10.1117/12.3026872

2 October 2024 Weight bank addition photonic accelerator for artificial intelligence

Wenwen Zhang, Hao Zhang, Jing Wang

Proceedings Volume 13113, Photonic Computing: From Materials and Devices to Systems and Applications; 131130B (2024) https://doi.org/10.1117/12.3026872
Event: Nanoscience + Engineering, 2024, San Diego, California, United States

Conference Poster

Abstract

Neural networks, a potent data-driven method for artificial intelligence, play a crucial role in modern estimation and classification tasks. However, they often encounter computational constraints. To overcome these, we propose photonics-based neuromorphic networks, offering faster processing than electronic systems. We focus on the weight bank, crucial for matrix multiplication, utilizing parallel cascaded micro-ring resonators (MRRs). Our study on silicon on insulators (SOI) demonstrates how cascaded MRRs address cross-talk issues in wavelength division multiplexing (WDM) systems. Additionally, we design a silicon photonic accelerator for weight addition, optimized for speed and energy efficiency, providing comparable performance to electronic devices.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wenwen Zhang, Hao Zhang, and Jing Wang "Weight bank addition photonic accelerator for artificial intelligence", Proc. SPIE 13113, Photonic Computing: From Materials and Devices to Systems and Applications, 131130B (2 October 2024); https://doi.org/10.1117/12.3026872

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